Air India ml engineer Interview Questions and Answers for Experienced

Data science, ML concepts, statistics, python output

Q1. What does correlation mean? what is the interpretation if the correlation is 0?
Ans. 
Correlation measures the strength and direction of a relationship between two variables. A correlation of 0 indicates no linear relationship.
• Correlation measures the degree to which two variables move in relation to each other. It ranges from -1 to 1.

• A correlation of 0 means there is no linear relationship between the variables. They are not related in a linear fashion.

• For example, if the correlation between hours of s...read more
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Q2. What is collinearity, how to handle it? also for multicollinearity
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Q3. How to evaluate regression models? explain r squared and adjusted r squared and difference between them
Ans. 
Regression models can be evaluated using R squared and adjusted R squared to measure the goodness of fit.
• R squared measures the proportion of the variance in the dependent variable that is predictable from the independent variables.

• Adjusted R squared adjusts for the number of predictors in the model, providing a more accurate measure of goodness of fit.

• R squared can be artificially inflated by adding more predictors, w...read more
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Q4. Explain regularization techniques, difference between ridge and lasso?
Ans. 
Regularization techniques help prevent overfitting in machine learning models. Ridge regression adds L2 regularization, while Lasso regression adds L1 regularization.
• Regularization techniques help prevent overfitting by adding a penalty term to the loss function.

• Ridge regression adds the squared magnitude of coefficients as penalty term (L2 regularization).

• Lasso regression adds the absolute magnitude of coefficients as...read more
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Q5. Explain working of decision trees, how to select parent and child nodes, gini impurity, etc?
Ans. 
Decision trees are a popular machine learning algorithm used for classification and regression tasks.
• Decision trees are a flowchart-like structure where each internal node represents a feature or attribute, each branch represents a decision rule, and each leaf node represents the outcome.

• To select parent and child nodes, the algorithm calculates the best split at each node based on criteria like Gini impurity or inform...read more
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Q6. Bagging and boosting and their difference, what is ensemble models, how to handle overfitting, explain precision recall roc curve
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Explanation of bagging, boosting, ensemble models, handling overfitting, and precision-recall-ROC curve.
• Bagging (Bootstrap Aggregating) involves training multiple models on different subsets of the training data and combining their predictions through averaging or voting.

• Boosting involves training multiple models sequentially, with each model correcting the errors of its predecessor.

• Ensemble models combine multiple ind...read more
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Q7. Create dictionary in python from 2 list and key values, sql query for window functions-Rank(), all joins in sql
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Creating dictionary in Python from 2 lists, using window functions and joins in SQL
• To create a dictionary in Python from 2 lists and key values, you can use the zip() function

• Example: dict(zip(keys_list, values_list))

• For SQL window functions like Rank(), you can use the OVER() clause

• Example: SELECT column1, column2, RANK() OVER(ORDER BY column3) AS rank_column FROM table_name

• For SQL joins, you can use INNER JOIN, LEFT ...read more
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